{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nest\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nest.ResetKernel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating nodes with spatial positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_grid = nest.spatial.grid(shape=[10, 10], extent=[2., 2.])\n",
    "\n",
    "nodes_grid = nest.Create('iaf_psc_alpha', positions=pos_grid)\n",
    "nest.PlotLayer(nodes_grid, nodesize=50)\n",
    "plt.gca().set_xticks((-1., -0.5, 0., 0.5, 1.))\n",
    "plt.gca().set_yticks((-1., -0.5, 0., 0.5, 1.));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_free_1 = nest.spatial.free(nest.random.uniform(-0.75, 0.75), extent=[2., 2.])\n",
    "\n",
    "nodes_free_1 = nest.Create('iaf_psc_alpha', 12, positions=pos_free_1)\n",
    "nest.PlotLayer(nodes_free_1, nodesize=50)\n",
    "plt.gca().set_xticks((-0.75, -0.25, 0.25, 0.75))\n",
    "plt.gca().set_yticks((-0.75, -0.25, 0.25, 0.75));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_free_2 = nest.spatial.free([nest.random.uniform(-0.75, 0.75),\n",
    "                           nest.random.uniform(-0.5, 0.5)],\n",
    "                          extent=[2., 1.5])\n",
    "nodes_free_2 = nest.Create('iaf_psc_exp', 50, positions=pos_free_2)\n",
    "nest.PlotLayer(nodes_free_2, nodesize=50);\n",
    "plt.gca().set_xticks((-0.75, -0.25, 0.25, 0.75))\n",
    "plt.gca().set_yticks((-0.5, 0, 0.5));\n",
    "\n",
    "pos_3 = nest.spatial.free([[-0.7, -0.7], [0.0, 0.0], [0.7, 0.7]])\n",
    "nodes_free_3 = nest.Create('iaf_psc_alpha', positions=pos_3)\n",
    "nest.PlotLayer(nodes_free_3, nodesize=50);\n",
    "plt.gca().set_xticks((-0.75, 0, 0.75))\n",
    "plt.gca().set_yticks((-0.75, 0, 0.75));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1 .. 112 iaf_psc_alpha\n",
      "113 .. 162 iaf_psc_exp\n",
      "163 .. 165 iaf_psc_alpha\n"
     ]
    }
   ],
   "source": [
    "nest.PrintNodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Connecting nodes based on spatial information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn_spec = {'rule': 'pairwise_bernoulli',\n",
    "             'p': 0.5}\n",
    "\n",
    "nest.Connect(nodes_free_1, nodes_free_2,\n",
    "             conn_spec=conn_spec,\n",
    "             syn_spec={'weight': nest.random.uniform(0.5, 2.)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src tgt wght delay\n",
      "101 113 1.56 1.0\n",
      "101 121 1.92 1.0\n",
      "101 123 1.79 1.0\n",
      "101 125 0.56 1.0\n",
      "101 120 1.21 1.0\n",
      "101 127 1.11 1.0\n",
      "101 128 1.59 1.0\n",
      "101 119 0.81 1.0\n",
      "101 135 1.46 1.0\n",
      "101 140 1.00 1.0\n",
      "101 141 0.93 1.0\n",
      "101 142 0.60 1.0\n",
      "101 143 1.40 1.0\n",
      "101 144 0.82 1.0\n",
      "101 145 0.85 1.0\n",
      "101 146 1.91 1.0\n",
      "101 147 1.40 1.0\n",
      "101 115 1.18 1.0\n",
      "101 148 1.95 1.0\n",
      "101 162 1.53 1.0\n",
      "101 159 1.38 1.0\n",
      "101 157 0.68 1.0\n",
      "101 154 0.75 1.0\n",
      "102 145 0.59 1.0\n",
      "102 113 1.27 1.0\n",
      "102 162 1.38 1.0\n",
      "102 125 2.00 1.0\n",
      "102 161 1.50 1.0\n",
      "102 126 0.77 1.0\n",
      "102 127 0.59 1.0\n",
      "102 158 0.91 1.0\n",
      "102 131 1.09 1.0\n",
      "102 135 1.11 1.0\n",
      "102 137 0.98 1.0\n",
      "102 138 0.93 1.0\n",
      "102 139 1.15 1.0\n",
      "102 153 0.90 1.0\n",
      "102 140 0.86 1.0\n",
      "102 152 1.02 1.0\n",
      "102 148 0.73 1.0\n",
      "102 143 1.33 1.0\n",
      "102 147 1.33 1.0\n",
      "102 120 1.68 1.0\n",
      "102 118 0.56 1.0\n",
      "102 115 0.54 1.0\n",
      "102 116 1.35 1.0\n",
      "102 117 1.89 1.0\n",
      "102 119 0.99 1.0\n",
      "103 132 0.67 1.0\n",
      "103 149 0.53 1.0\n",
      "103 148 0.73 1.0\n",
      "103 126 0.98 1.0\n",
      "103 147 0.86 1.0\n",
      "103 145 1.97 1.0\n",
      "103 143 1.29 1.0\n",
      "103 116 0.53 1.0\n",
      "103 142 0.66 1.0\n",
      "103 128 1.66 1.0\n",
      "103 140 0.59 1.0\n",
      "103 117 0.61 1.0\n",
      "103 130 1.84 1.0\n",
      "103 139 1.76 1.0\n",
      "103 137 1.84 1.0\n",
      "103 136 1.02 1.0\n",
      "103 135 1.06 1.0\n",
      "103 162 1.96 1.0\n",
      "103 122 1.72 1.0\n",
      "103 114 1.61 1.0\n",
      "103 151 1.78 1.0\n",
      "103 160 1.22 1.0\n",
      "103 157 1.08 1.0\n",
      "103 152 1.12 1.0\n",
      "103 150 1.30 1.0\n",
      "103 155 1.32 1.0\n",
      "104 156 1.96 1.0\n",
      "104 123 1.63 1.0\n",
      "104 127 1.44 1.0\n",
      "104 153 1.75 1.0\n",
      "104 131 1.25 1.0\n",
      "104 120 0.57 1.0\n",
      "104 122 1.23 1.0\n",
      "104 138 1.29 1.0\n",
      "104 130 1.60 1.0\n",
      "104 144 1.29 1.0\n",
      "104 136 1.00 1.0\n",
      "104 145 1.68 1.0\n",
      "104 113 0.89 1.0\n",
      "104 152 1.43 1.0\n",
      "104 147 1.77 1.0\n",
      "104 121 1.30 1.0\n",
      "104 149 1.34 1.0\n",
      "104 133 0.86 1.0\n",
      "105 160 1.53 1.0\n",
      "105 136 1.80 1.0\n",
      "105 141 0.76 1.0\n",
      "105 133 1.57 1.0\n",
      "105 137 1.77 1.0\n",
      "105 155 1.61 1.0\n",
      "105 159 0.62 1.0\n",
      "105 157 0.77 1.0\n",
      "105 139 0.90 1.0\n",
      "105 140 1.43 1.0\n",
      "105 156 0.79 1.0\n",
      "105 142 0.65 1.0\n",
      "105 144 1.61 1.0\n",
      "105 115 0.82 1.0\n",
      "105 152 1.58 1.0\n",
      "105 151 0.56 1.0\n",
      "105 148 1.32 1.0\n",
      "105 149 1.53 1.0\n",
      "105 150 0.52 1.0\n",
      "105 125 1.15 1.0\n",
      "105 121 0.93 1.0\n",
      "105 132 1.45 1.0\n",
      "105 129 1.54 1.0\n",
      "105 128 0.61 1.0\n",
      "105 126 1.99 1.0\n",
      "105 127 0.69 1.0\n",
      "105 120 1.18 1.0\n",
      "105 123 1.94 1.0\n",
      "106 127 1.43 1.0\n",
      "106 125 1.67 1.0\n",
      "106 146 1.48 1.0\n",
      "106 143 1.77 1.0\n",
      "106 162 1.50 1.0\n",
      "106 116 1.65 1.0\n",
      "106 141 1.55 1.0\n",
      "106 117 1.20 1.0\n",
      "106 138 1.64 1.0\n",
      "106 149 1.62 1.0\n",
      "106 114 1.66 1.0\n",
      "106 124 0.89 1.0\n",
      "106 123 1.09 1.0\n",
      "106 152 1.93 1.0\n",
      "106 156 0.95 1.0\n",
      "106 122 1.57 1.0\n",
      "106 158 1.08 1.0\n",
      "106 121 1.12 1.0\n",
      "106 160 0.85 1.0\n",
      "106 161 0.83 1.0\n",
      "106 113 0.61 1.0\n",
      "106 126 0.85 1.0\n",
      "106 136 0.97 1.0\n",
      "106 134 1.06 1.0\n",
      "106 118 1.60 1.0\n",
      "106 135 0.96 1.0\n",
      "107 161 1.69 1.0\n",
      "107 141 1.39 1.0\n",
      "107 142 1.31 1.0\n",
      "107 131 0.77 1.0\n",
      "107 145 1.50 1.0\n",
      "107 126 1.86 1.0\n",
      "107 148 0.81 1.0\n",
      "107 150 1.88 1.0\n",
      "107 124 1.91 1.0\n",
      "107 152 0.83 1.0\n",
      "107 154 1.55 1.0\n",
      "107 155 0.63 1.0\n",
      "107 156 0.75 1.0\n",
      "107 160 1.33 1.0\n",
      "107 113 0.82 1.0\n",
      "107 149 1.72 1.0\n",
      "107 139 1.42 1.0\n",
      "107 138 1.30 1.0\n",
      "108 135 1.56 1.0\n",
      "108 131 0.87 1.0\n",
      "108 115 0.56 1.0\n",
      "108 146 1.91 1.0\n",
      "108 134 1.26 1.0\n",
      "108 151 1.01 1.0\n",
      "108 157 0.53 1.0\n",
      "108 147 0.71 1.0\n",
      "108 130 0.52 1.0\n",
      "108 113 1.68 1.0\n",
      "108 144 1.77 1.0\n",
      "108 158 1.36 1.0\n",
      "108 119 1.97 1.0\n",
      "108 133 0.94 1.0\n",
      "108 139 1.50 1.0\n",
      "108 148 0.87 1.0\n",
      "108 156 0.94 1.0\n",
      "108 149 1.53 1.0\n",
      "108 127 1.52 1.0\n",
      "108 137 1.66 1.0\n",
      "108 125 1.92 1.0\n",
      "108 116 1.59 1.0\n",
      "108 162 0.78 1.0\n",
      "108 141 0.90 1.0\n",
      "109 157 1.26 1.0\n",
      "109 122 1.01 1.0\n",
      "109 135 1.28 1.0\n",
      "109 150 1.26 1.0\n",
      "109 130 1.62 1.0\n",
      "109 158 1.40 1.0\n",
      "109 125 1.24 1.0\n",
      "109 145 1.80 1.0\n",
      "109 126 1.82 1.0\n",
      "109 162 1.67 1.0\n",
      "109 134 1.98 1.0\n",
      "109 155 0.89 1.0\n",
      "109 144 1.97 1.0\n",
      "109 133 0.58 1.0\n",
      "109 121 0.51 1.0\n",
      "109 137 1.30 1.0\n",
      "109 132 0.85 1.0\n",
      "110 149 1.13 1.0\n",
      "110 153 1.26 1.0\n",
      "110 117 0.83 1.0\n",
      "110 123 0.53 1.0\n",
      "110 131 1.35 1.0\n",
      "110 156 1.28 1.0\n",
      "110 122 0.53 1.0\n",
      "110 124 1.95 1.0\n",
      "110 113 1.95 1.0\n",
      "110 128 1.87 1.0\n",
      "110 127 1.98 1.0\n",
      "110 120 0.90 1.0\n",
      "110 142 0.62 1.0\n",
      "110 136 1.24 1.0\n",
      "110 119 0.85 1.0\n",
      "110 160 1.26 1.0\n",
      "110 115 1.81 1.0\n",
      "110 154 0.90 1.0\n",
      "110 133 0.50 1.0\n",
      "110 145 0.71 1.0\n",
      "110 138 1.44 1.0\n",
      "110 134 0.66 1.0\n",
      "110 132 1.01 1.0\n",
      "110 159 1.53 1.0\n",
      "110 121 1.68 1.0\n",
      "110 125 1.51 1.0\n",
      "110 137 1.54 1.0\n",
      "111 113 1.82 1.0\n",
      "111 155 1.84 1.0\n",
      "111 122 0.69 1.0\n",
      "111 162 1.83 1.0\n",
      "111 159 0.90 1.0\n",
      "111 133 1.09 1.0\n",
      "111 117 1.02 1.0\n",
      "111 160 1.55 1.0\n",
      "111 137 0.60 1.0\n",
      "111 151 0.51 1.0\n",
      "111 126 0.59 1.0\n",
      "111 154 0.77 1.0\n",
      "111 145 1.51 1.0\n",
      "111 153 1.26 1.0\n",
      "111 134 1.81 1.0\n",
      "111 152 1.03 1.0\n",
      "111 114 1.08 1.0\n",
      "111 139 0.64 1.0\n",
      "111 148 1.91 1.0\n",
      "111 135 0.92 1.0\n",
      "111 129 1.13 1.0\n",
      "111 142 0.53 1.0\n",
      "111 124 1.43 1.0\n",
      "111 130 1.59 1.0\n",
      "112 118 0.53 1.0\n",
      "112 126 0.56 1.0\n",
      "112 137 1.19 1.0\n",
      "112 144 1.47 1.0\n",
      "112 115 1.92 1.0\n",
      "112 139 1.75 1.0\n",
      "112 161 0.70 1.0\n",
      "112 128 0.51 1.0\n",
      "112 132 1.89 1.0\n",
      "112 141 1.59 1.0\n",
      "112 117 1.41 1.0\n",
      "112 120 0.99 1.0\n",
      "112 162 0.57 1.0\n",
      "112 119 0.73 1.0\n",
      "112 145 1.36 1.0\n",
      "112 135 1.90 1.0\n",
      "112 122 1.04 1.0\n",
      "112 147 1.94 1.0\n",
      "112 114 1.54 1.0\n",
      "112 157 0.55 1.0\n",
      "112 124 1.18 1.0\n",
      "112 121 1.95 1.0\n",
      "112 130 1.32 1.0\n",
      "112 151 1.25 1.0\n",
      "112 131 1.16 1.0\n",
      "112 138 0.82 1.0\n",
      "112 113 1.32 1.0\n",
      "112 154 0.97 1.0\n"
     ]
    }
   ],
   "source": [
    "conns = nest.GetConnections()\n",
    "print('src tgt wght delay')\n",
    "for conn in conns:\n",
    "    src = conn.source\n",
    "    tgt = conn.target\n",
    "    wght = conn.weight\n",
    "    delay = conn.delay\n",
    "    print('{} {} {:.2f} {}'.format(src, tgt, wght, delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conns.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*--------*-------------------------------------------------------------------------------------------*\n",
      "| source | 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, |\n",
      "*--------*-------------------------------------------------------------------------------------------*\n",
      "| target | 113, 121, 126, 127, 132, 133, 135, 136, 138, 141, 143, 145, 147, 149, 154, 156, 159, 161, |\n",
      "*--------*-------------------------------------------------------------------------------------------*\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 1.0 - 1.5 * nest.spatial.distance + 0.2*nest.random.uniform()\n",
    "conn_spec = {'rule': 'pairwise_bernoulli',\n",
    "             'p': p}\n",
    "\n",
    "center_node = nest.Create('iaf_psc_exp', 12, positions=nest.spatial.free([[0., 0.]]))\n",
    "nest.Connect(center_node, nodes_free_2,\n",
    "             conn_spec=conn_spec,\n",
    "             syn_spec={'weight': nest.random.uniform(0.5, 2.)})\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "print(nest.GetConnections(source=center_node, target=nodes_free_2))\n",
    "nest.PlotTargets(center_node, nodes_free_2, probability_parameter=p, fig=fig);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = nest.logic.conditional(nest.spatial.target_pos.x > 0.0,\n",
    "                           nest.spatial_distributions.exponential(nest.spatial.distance, beta=0.5),\n",
    "                           0.8*nest.math.exp(nest.spatial.target_pos.x))\n",
    "\n",
    "conn_spec = {'rule': 'pairwise_bernoulli',\n",
    "             'p': p}\n",
    "\n",
    "nest.Connect(center_node, nodes_free_1,\n",
    "             conn_spec=conn_spec,\n",
    "             syn_spec={'weight': nest.random.uniform(0.5, 2.)})\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "nest.PlotTargets(center_node, nodes_free_1, probability_parameter=p, fig=fig);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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